{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. \n",
    "One idea along these lines is batch normalization which was proposed by [1] in 2015.\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However, even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [1] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [1] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "=========== You can safely ignore the message below if you are NOT working on ConvolutionalNetworks.ipynb ===========\n\tYou will need to compile a Cython extension for a portion of this assignment.\n\tThe instructions to do this will be given in a section of the notebook below.\n\tThere will be an option for Colab users and another for Jupyter (local) users.\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n",
    "\n",
    "def print_mean_std(x,axis=0):\n",
    "    print('  means: ', x.mean(axis=axis))\n",
    "    print('  stds:  ', x.std(axis=axis))\n",
    "    print() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "X_train:  (49000, 3, 32, 32)\ny_train:  (49000,)\nX_val:  (1000, 3, 32, 32)\ny_val:  (1000,)\nX_test:  (1000, 3, 32, 32)\ny_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.\n",
    "\n",
    "Referencing the paper linked to above in [1] may be helpful!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:   [27.18502186 34.21455511 37.68611762]\n",
      "\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  means:  [ 1.77635684e-17  6.10622664e-17 -1.86656246e-17]\n",
      "  stds:   [0.99999999 1.         1.        ]\n",
      "\n",
      "After batch normalization (gamma= [1. 2. 3.] , beta= [11. 12. 13.] )\n",
      "  means:  [11. 12. 13.]\n",
      "  stds:   [0.99999999 1.99999999 2.99999999]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print_mean_std(a,axis=0)\n",
    "\n",
    "gamma = np.ones((D3,))\n",
    "beta = np.zeros((D3,))\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)\n",
    "\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After batch normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "After batch normalization (test-time):\n  means:  [-0.03927354 -0.04349152 -0.10452688]\n  stds:   [1.01531428 1.01238373 0.97819988]\n\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "\n",
    "for t in range(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "dx error:  1.7029258328157158e-09\ndgamma error:  7.420414216247087e-13\ndbeta error:  2.8795057655839487e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "#You should expect to see relative errors between 1e-13 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: alternative backward\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For example, you can derive a very simple formula for the sigmoid function's backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can do a similar simplification for the batch normalization backward pass too!  \n",
    "\n",
    "In the forward pass, given a set of inputs $X=\\begin{bmatrix}x_1\\\\x_2\\\\...\\\\x_N\\end{bmatrix}$, \n",
    "\n",
    "we first calculate the mean $\\mu$ and variance $v$.\n",
    "With $\\mu$ and $v$ calculated, we can calculate the standard deviation $\\sigma$  and normalized data $Y$.\n",
    "The equations and graph illustration below describe the computation ($y_i$ is the i-th element of the vector $Y$).\n",
    "\n",
    "\\begin{align}\n",
    "& \\mu=\\frac{1}{N}\\sum_{k=1}^N x_k  &  v=\\frac{1}{N}\\sum_{k=1}^N (x_k-\\mu)^2 \\\\\n",
    "& \\sigma=\\sqrt{v+\\epsilon}         &  y_i=\\frac{x_i-\\mu}{\\sigma}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/cs231n/cs231n.github.io/master/assets/a2/batchnorm_graph.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "The meat of our problem during backpropagation is to compute $\\frac{\\partial L}{\\partial X}$, given the upstream gradient we receive, $\\frac{\\partial L}{\\partial Y}.$ To do this, recall the chain rule in calculus gives us $\\frac{\\partial L}{\\partial X} = \\frac{\\partial L}{\\partial Y} \\cdot \\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "The unknown/hart part is $\\frac{\\partial Y}{\\partial X}$. We can find this by first deriving step-by-step our local gradients at \n",
    "$\\frac{\\partial v}{\\partial X}$, $\\frac{\\partial \\mu}{\\partial X}$,\n",
    "$\\frac{\\partial \\sigma}{\\partial v}$, \n",
    "$\\frac{\\partial Y}{\\partial \\sigma}$, and $\\frac{\\partial Y}{\\partial \\mu}$,\n",
    "and then use the chain rule to compose these gradients (which appear in the form of vectors!) appropriately to compute $\\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "If it's challenging to directly reason about the gradients over $X$ and $Y$ which require matrix multiplication, try reasoning about the gradients in terms of individual elements $x_i$ and $y_i$ first: in that case, you will need to come up with the derivations for $\\frac{\\partial L}{\\partial x_i}$, by relying on the Chain Rule to first calculate the intermediate $\\frac{\\partial \\mu}{\\partial x_i}, \\frac{\\partial v}{\\partial x_i}, \\frac{\\partial \\sigma}{\\partial x_i},$ then assemble these pieces to calculate $\\frac{\\partial y_i}{\\partial x_i}$. \n",
    "\n",
    "You should make sure each of the intermediary gradient derivations are all as simplified as possible, for ease of implementation. \n",
    "\n",
    "After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "dx difference:  3.066681237413518e-13\ndgamma difference:  0.0\ndbeta difference:  0.0\nspeedup: 1.00x\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs231n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the `normalization` flag is set to `\"batchnorm\"` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.2611955101340957\n",
      "W1 relative error: 1.10e-04\n",
      "W2 relative error: 2.85e-06\n",
      "W3 relative error: 3.92e-10\n",
      "b1 relative error: 2.22e-03\n",
      "b2 relative error: 2.22e-08\n",
      "b3 relative error: 4.78e-11\n",
      "beta1 relative error: 7.33e-09\n",
      "beta2 relative error: 1.89e-09\n",
      "gamma1 relative error: 7.47e-09\n",
      "gamma2 relative error: 2.41e-09\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.996533220108303\n",
      "W1 relative error: 1.98e-06\n",
      "W2 relative error: 2.28e-06\n",
      "W3 relative error: 1.11e-08\n",
      "b1 relative error: 5.55e-09\n",
      "b2 relative error: 2.22e-08\n",
      "b3 relative error: 2.64e-10\n",
      "beta1 relative error: 6.65e-09\n",
      "beta2 relative error: 3.48e-09\n",
      "gamma1 relative error: 5.94e-09\n",
      "gamma2 relative error: 3.72e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "# You should expect losses between 1e-4~1e-10 for W, \n",
    "# losses between 1e-08~1e-10 for b,\n",
    "# and losses between 1e-08~1e-09 for beta and gammas.\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            normalization='batchnorm')\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Solver with batch norm:\n",
      "(Iteration 1 / 200) loss: 2.340974\n",
      "(Epoch 0 / 10) train acc: 0.107000; val_acc: 0.115000\n",
      "(Epoch 1 / 10) train acc: 0.313000; val_acc: 0.266000\n",
      "(Iteration 21 / 200) loss: 2.039365\n",
      "(Epoch 2 / 10) train acc: 0.390000; val_acc: 0.279000\n",
      "(Iteration 41 / 200) loss: 2.036710\n",
      "(Epoch 3 / 10) train acc: 0.497000; val_acc: 0.317000\n",
      "(Iteration 61 / 200) loss: 1.769763\n",
      "(Epoch 4 / 10) train acc: 0.536000; val_acc: 0.312000\n",
      "(Iteration 81 / 200) loss: 1.267646\n",
      "(Epoch 5 / 10) train acc: 0.596000; val_acc: 0.321000\n",
      "(Iteration 101 / 200) loss: 1.281064\n",
      "(Epoch 6 / 10) train acc: 0.649000; val_acc: 0.323000\n",
      "(Iteration 121 / 200) loss: 1.119860\n",
      "(Epoch 7 / 10) train acc: 0.689000; val_acc: 0.335000\n",
      "(Iteration 141 / 200) loss: 1.161137\n",
      "(Epoch 8 / 10) train acc: 0.705000; val_acc: 0.294000\n",
      "(Iteration 161 / 200) loss: 0.788523\n",
      "(Epoch 9 / 10) train acc: 0.785000; val_acc: 0.331000\n",
      "(Iteration 181 / 200) loss: 0.863694\n",
      "(Epoch 10 / 10) train acc: 0.786000; val_acc: 0.316000\n",
      "\n",
      "Solver without batch norm:\n",
      "(Iteration 1 / 200) loss: 2.302332\n",
      "(Epoch 0 / 10) train acc: 0.129000; val_acc: 0.131000\n",
      "(Epoch 1 / 10) train acc: 0.283000; val_acc: 0.250000\n",
      "(Iteration 21 / 200) loss: 2.041970\n",
      "(Epoch 2 / 10) train acc: 0.316000; val_acc: 0.277000\n",
      "(Iteration 41 / 200) loss: 1.900473\n",
      "(Epoch 3 / 10) train acc: 0.373000; val_acc: 0.282000\n",
      "(Iteration 61 / 200) loss: 1.713156\n",
      "(Epoch 4 / 10) train acc: 0.390000; val_acc: 0.310000\n",
      "(Iteration 81 / 200) loss: 1.662209\n",
      "(Epoch 5 / 10) train acc: 0.434000; val_acc: 0.300000\n",
      "(Iteration 101 / 200) loss: 1.696061\n",
      "(Epoch 6 / 10) train acc: 0.536000; val_acc: 0.346000\n",
      "(Iteration 121 / 200) loss: 1.550519\n",
      "(Epoch 7 / 10) train acc: 0.538000; val_acc: 0.309000\n",
      "(Iteration 141 / 200) loss: 1.433871\n",
      "(Epoch 8 / 10) train acc: 0.626000; val_acc: 0.344000\n",
      "(Iteration 161 / 200) loss: 1.013332\n",
      "(Epoch 9 / 10) train acc: 0.653000; val_acc: 0.330000\n",
      "(Iteration 181 / 200) loss: 0.923830\n",
      "(Epoch 10 / 10) train acc: 0.726000; val_acc: 0.334000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "print('Solver with batch norm:')\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True,print_every=20)\n",
    "bn_solver.train()\n",
    "\n",
    "print('\\nSolver without batch norm:')\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "def plot_training_history(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.', labels=None):\n",
    "    \"\"\"utility function for plotting training history\"\"\"\n",
    "    plt.title(title)\n",
    "    plt.xlabel(label)\n",
    "    bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]\n",
    "    bl_plot = plot_fn(baseline)\n",
    "    num_bn = len(bn_plots)\n",
    "    for i in range(num_bn):\n",
    "        label='with_norm'\n",
    "        if labels is not None:\n",
    "            label += str(labels[i])\n",
    "        plt.plot(bn_plots[i], bn_marker, label=label)\n",
    "    label='baseline'\n",
    "    if labels is not None:\n",
    "        label += str(labels[0])\n",
    "    plt.plot(bl_plot, bl_marker, label=label)\n",
    "    plt.legend(loc='lower center', ncol=num_bn+1) \n",
    "\n",
    "    \n",
    "plt.subplot(3, 1, 1)\n",
    "plot_training_history('Training loss','Iteration', solver, [bn_solver], \\\n",
    "                      lambda x: x.loss_history, bl_marker='o', bn_marker='o')\n",
    "plt.subplot(3, 1, 2)\n",
    "plot_training_history('Training accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "plt.subplot(3, 1, 3)\n",
    "plot_training_history('Validation accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers_ws = {}\n",
    "solvers_ws = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "    print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "    bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "    bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    bn_solver.train()\n",
    "    bn_solvers_ws[weight_scale] = bn_solver\n",
    "\n",
    "    solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    solver.train()\n",
    "    solvers_ws[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1080x1080 with 3 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers_ws[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers_ws[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers_ws[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers_ws[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers_ws[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers_ws[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 1:\n",
    "Describe the results of this experiment. How does the scale of weight initialization affect models with/without batch normalization differently, and why?\n",
    "\n",
    "## Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and batch size\n",
    "We will now run a small experiment to study the interaction of batch normalization and batch size.\n",
    "\n",
    "The first cell will train 6-layer networks both with and without batch normalization using different batch sizes. The second layer will plot training accuracy and validation set accuracy over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    }
   ],
   "source": [
    "def run_batchsize_experiments(normalization_mode):\n",
    "    np.random.seed(231)\n",
    "    # Try training a very deep net with batchnorm\n",
    "    hidden_dims = [100, 100, 100, 100, 100]\n",
    "    num_train = 1000\n",
    "    small_data = {\n",
    "      'X_train': data['X_train'][:num_train],\n",
    "      'y_train': data['y_train'][:num_train],\n",
    "      'X_val': data['X_val'],\n",
    "      'y_val': data['y_val'],\n",
    "    }\n",
    "    n_epochs=10\n",
    "    weight_scale = 2e-2\n",
    "    batch_sizes = [5,10,50]\n",
    "    lr = 10**(-3.5)\n",
    "    solver_bsize = batch_sizes[0]\n",
    "\n",
    "    print('No normalization: batch size = ',solver_bsize)\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "    solver = Solver(model, small_data,\n",
    "                    num_epochs=n_epochs, batch_size=solver_bsize,\n",
    "                    update_rule='adam',\n",
    "                    optim_config={\n",
    "                      'learning_rate': lr,\n",
    "                    },\n",
    "                    verbose=False)\n",
    "    solver.train()\n",
    "    \n",
    "    bn_solvers = []\n",
    "    for i in range(len(batch_sizes)):\n",
    "        b_size=batch_sizes[i]\n",
    "        print('Normalization: batch size = ',b_size)\n",
    "        bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n",
    "        bn_solver = Solver(bn_model, small_data,\n",
    "                        num_epochs=n_epochs, batch_size=b_size,\n",
    "                        update_rule='adam',\n",
    "                        optim_config={\n",
    "                          'learning_rate': lr,\n",
    "                        },\n",
    "                        verbose=False)\n",
    "        bn_solver.train()\n",
    "        bn_solvers.append(bn_solver)\n",
    "        \n",
    "    return bn_solvers, solver, batch_sizes\n",
    "\n",
    "batch_sizes = [5,10,50]\n",
    "bn_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('batchnorm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 2:\n",
    "Describe the results of this experiment. What does this imply about the relationship between batch normalization and batch size? Why is this relationship observed?\n",
    "\n",
    "## Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization\n",
    "Batch normalization has proved to be effective in making networks easier to train, but the dependency on batch size makes it less useful in complex networks which have a cap on the input batch size due to hardware limitations. \n",
    "\n",
    "Several alternatives to batch normalization have been proposed to mitigate this problem; one such technique is Layer Normalization [2]. Instead of normalizing over the batch, we normalize over the features. In other words, when using Layer Normalization, each feature vector corresponding to a single datapoint is normalized based on the sum of all terms within that feature vector.\n",
    "\n",
    "[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 3:\n",
    "Which of these data preprocessing steps is analogous to batch normalization, and which is analogous to layer normalization?\n",
    "\n",
    "1. Scaling each image in the dataset, so that the RGB channels for each row of pixels within an image sums up to 1.\n",
    "2. Scaling each image in the dataset, so that the RGB channels for all pixels within an image sums up to 1.  \n",
    "3. Subtracting the mean image of the dataset from each image in the dataset.\n",
    "4. Setting all RGB values to either 0 or 1 depending on a given threshold.\n",
    "\n",
    "## Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization: Implementation\n",
    "\n",
    "Now you'll implement layer normalization. This step should be relatively straightforward, as conceptually the implementation is almost identical to that of batch normalization. One significant difference though is that for layer normalization, we do not keep track of the moving moments, and the testing phase is identical to the training phase, where the mean and variance are directly calculated per datapoint.\n",
    "\n",
    "Here's what you need to do:\n",
    "\n",
    "* In `cs231n/layers.py`, implement the forward pass for layer normalization in the function `layernorm_forward`. \n",
    "\n",
    "Run the cell below to check your results.\n",
    "* In `cs231n/layers.py`, implement the backward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the second cell below to check your results.\n",
    "* Modify `cs231n/classifiers/fc_net.py` to add layer normalization to the `FullyConnectedNet`. When the `normalization` flag is set to `\"layernorm\"` in the constructor, you should insert a layer normalization layer before each ReLU nonlinearity. \n",
    "\n",
    "Run the third cell below to run the batch size experiment on layer normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Before layer normalization:\n  means:  [-59.06673243 -47.60782686 -43.31137368 -26.40991744]\n  stds:   [10.07429373 28.39478981 35.28360729  4.01831507]\n\nAfter layer normalization (gamma=1, beta=0)\n  means:  [ 7.40148683e-16 -7.40148683e-17  0.00000000e+00  2.96059473e-16]\n  stds:   [0.99999995 0.99999999 1.         0.99999969]\n\nAfter layer normalization (gamma= [3. 3. 3.] , beta= [5. 5. 5.] )\n  means:  [5. 5. 5. 5.]\n  stds:   [2.99999985 2.99999998 2.99999999 2.99999907]\n\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after layer normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 =4, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before layer normalization:')\n",
    "print_mean_std(a,axis=1)\n",
    "\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After layer normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)\n",
    "\n",
    "gamma = np.asarray([3.0,3.0,3.0])\n",
    "beta = np.asarray([5.0,5.0,5.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After layer normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "dx error:  1.433615657860454e-09\ndgamma error:  4.519489546032799e-12\ndbeta error:  2.276445013433725e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "ln_param = {}\n",
    "fx = lambda x: layernorm_forward(x, gamma, beta, ln_param)[0]\n",
    "fg = lambda a: layernorm_forward(x, a, beta, ln_param)[0]\n",
    "fb = lambda b: layernorm_forward(x, gamma, b, ln_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = layernorm_forward(x, gamma, beta, ln_param)\n",
    "dx, dgamma, dbeta = layernorm_backward(dout, cache)\n",
    "\n",
    "#You should expect to see relative errors between 1e-12 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization and batch size\n",
    "\n",
    "We will now run the previous batch size experiment with layer normalization instead of batch normalization. Compared to the previous experiment, you should see a markedly smaller influence of batch size on the training history!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1080x720 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "ln_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('layernorm')\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 4:\n",
    "When is layer normalization likely to not work well, and why?\n",
    "\n",
    "1. Using it in a very deep network\n",
    "2. Having a very small dimension of features\n",
    "3. Having a high regularization term\n",
    "\n",
    "\n",
    "## Answer:\n",
    "[FILL THIS IN]\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}